We all know AI is growing very fast and a part of our Elastik Team’s culture ‘Learn and Grow’, we learn something new every quarter. In Q4 of 2024, we made some really good progress in our AI custom application ‘AI Wizard’ to convert it from RAG based framework to the Multi-Agent Framework. We had so many discussions and brainstorming sessions around it to finalize the design and the frameworks to use for it and finally chose two multi-Agent frameworks ‘AutGen’ and ‘LangGraph’ to start with so that we can compare the different parameters between them like performance, ease of use, time required to build the new features etc. So, we decided on two teams and provided them with two different learning paths to start learning on these frameworks and build one POC each to understand how it works. Once we got confidence, we started the actual implementation and converted the application within the sprint of 3 weeks successfully. As I was also involved in this conversion project, I was also kept a goal to learn the AI Agent and Multi-Agents framework, and in this blog, I would like to write some details on what are these frameworks and what are their key features. So, let’s start.
What Are These Frameworks?
- AutoGen: It’s a user-friendly framework designed to simplify the process of creating multi-agent systems. It focuses on easy integration and customization, making it ideal for beginners and smaller teams.
- LangGraph: This framework emphasizes structure and scalability, allowing developers to create complex systems with a clear flow of communication between agents. It’s great for large projects where you need detailed control over agent interactions.
What are the Key Features?
1. Ease of Use
- AutoGen: This is a plug-and-play experience. Using this you can quickly set up agents with vey less coding. When you have a short time, this is a perfect tool.
- LangGraph: This requires comparatively more initial setup, and you will need to define the detailed workflows for this.
2. Flexibility
- AutoGen: This provides the pre-built templates and tools, and hence it may feel like limited for the customization.
- LangGraph: This is the most flexible and hence most of the customizations are possible with this.
3. Scalability
- AutoGen: This is mostly suitable for small to medium projects. For large projects might require additional workarounds.
- LangGraph: As this is mainly require for the flexible and customizable applications, even if you have number of agents or workflows it’s possible with this.
4. Learning Curve
- AutoGen: As this require minimal coding and as it is based on the pre-built tools it is easy to learn.
- LangGraph: It has a learning curve but it add really good value in the developer’s knowledge base.
When to Choose What?
Use AutoGen if:
- You want to finish the project quickly.
- You are new to AI development.
- Your project is small.
Use LangGraph if:
- You are working on a large and complex system.
- You are comfortable with more advanced coding.
What is the takeaway?
AutoGen and LangGraph are both very powerful tools to build multi-agent frameworks, and both has their own key features. As per my analysis, based on your project requirements you can choose either of them. If your project is small, easy and if the developers are new then you can use AutoGen, where on other side if the project is largen with lots of scenarios and if your developers are skilled, then you can choose LangGraph!
Happy Coding!
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